A dynamic MOPSO algorithm for multiobjective optimal design of hybrid renewable energy systems

Sharafi, Masoud; ElMekkawy, Tarek Y.

doi:10.1002/er.3202

Cited by 47 publications

(31 citation statements)

References 27 publications

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“…The generated PF is evaluated by three wellknown performance metrics that will be explained in Section 5. This paper is the extension of the previous work [18] in which DMOPSO algorithm was demonstrated for optimal sizing of HRESs. In this study, the main aim is to utilize the designed Pareto-based MOP algorithm in a new design problem.…”

Section: Introductionmentioning

confidence: 90%

“…In the cell-based density calculation strategy, the objective space is divided to hypercube and solutions are distributed over the resulted grid [29]. It allows using the density information to achieve diversity in the swarm [18]. For detail explanation of the algorithm, readers are referred to the previous work [18].…”

Section: Dynamic Multi Objective Particle Swarm Optimization Algorithmentioning

confidence: 99%

“…The main idea of MOPSO is that the output of PSO is a set of solutions which are named non-dominated solutions or Pareto front (PF). In this study, dynamic multi-objective particle swarm optimization (DMOPSO) algorithm, which was developed in the previous work of the authors [18], has been applied to simultaneously minimize total NPC of the system, fuel emission and maximize RER. The engaging advantage of this method is generating a PF with higher quality than what is resulted by well-known MOP approaches.…”

Section: Dynamic Multi Objective Particle Swarm Optimization Algorithmentioning

confidence: 99%

“…The main idea of the proposed algorithm is that multi-leaders and dynamic cell-based density calculation strategy is utilized to update the solutions. Two methods usually are performed to select leaders in guiding swarm flights: randomly selecting one of the best solutions or using multiple leaders [18]. In the cell-based density calculation strategy, the objective space is divided to hypercube and solutions are distributed over the resulted grid [29].…”

Section: Dynamic Multi Objective Particle Swarm Optimization Algorithmentioning

confidence: 99%

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Optimal design of hybrid renewable energy systems in buildings with low to high renewable energy ratio

2015

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a b s t r a c tWe develop a simulation-based meta-heuristic approach that determines the optimal size of a hybrid renewable energy system for residential buildings. This multi-objective optimization problem requires the advancement of a dynamic multi-objective particle swarm optimization algorithm that maximizes the renewable energy ratio of buildings and minimizes total net present cost and CO 2 emission for required system changes. Three proven performance metrics evaluate the quality of the Pareto front generated by the proposed approach. The obtained results are compared against two reported multiobjective optimization algorithms in the related literature. Finally, an existing residential apartment located in a cold Canadian climate provides a test case to apply the proposed model and optimally size a hybrid renewable energy system. In this test application, the model investigates the potential use of a heat pump, a biomass boiler, wind turbines, solar heat collectors, photovoltaic panels, and a heat storage tank to produce renewable energy for the building. Furthermore, the utilization of plug-in electric vehicles for transportation reduces gasoline use where all power is generated by the building, and the utility provides the means to match intermittent renewable generation from solar and wind to the building electrical loads. Model results show that under the chosen meteorological conditions and building parameters a wind turbine, and plug-in electric vehicle technologies are consistently the optimal option to achieve a target renewable energy ratio. In particular, the optimization result shows that the renewable energy ratio can achieve near 100% by installing a 73 kW wind turbine, a 200 kW biomass boiler, and using plug-in electric vehicles. This option has a net present cost of C$705,180 and results in total CO 2 emission of 2.4 ton/year. Finally, a sensitivity analysis is performed to investigate the impact of economic constants on net present cost of the obtained non-dominated solutions.

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Section: Introductionmentioning

confidence: 90%

Section: Dynamic Multi Objective Particle Swarm Optimization Algorithmentioning

confidence: 99%

Section: Dynamic Multi Objective Particle Swarm Optimization Algorithmentioning

confidence: 99%

Section: Dynamic Multi Objective Particle Swarm Optimization Algorithmentioning

confidence: 99%

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Optimal design of hybrid renewable energy systems in buildings with low to high renewable energy ratio

2015

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“…The main goal of the design is to minimize simultaneously the total net present cost of the system, unmet load, and carbon dioxide emissions [23].…”

Section: Multiobjective Optimization Of Renewable Energy Systemsmentioning

confidence: 99%

Optimization of a wind powered desalination and pumped hydro storage system

et al. 2016

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h i g h l i g h t sOperational strategy and sizing optimization of an integrated power and water supply system. Multiobjective optimization using the Direct Multisearch method. Significant increase in RES production of total electricity production is feasible. Significant cost and CO 2 emissions reductions are achievable. a b s t r a c tThe penetration of intermittent renewable energy sources, for instances wind power, in the power system of isolated islands is limited, even when there is large potential. The wind power that cannot be directly injected in the power grid is usually curtailed. In addition, some islands need to desalinate seawater to produce fresh water, increasing the pressure on the power system, because desalination needs electricity. Nevertheless, the water scarcity problem of an island can be part of the solution of the problem of its integration of intermittent renewable energy sources. To tackle this issue, a system was proposed to use the excess wind power in desalination units and in a pumped hydro storage, resulting in an integrated power and water supply system that would minimize the wind power curtailed. This paper proposes a methodology to optimize the size and operational strategy of this wind powered desalination and pumped hydro storage system. The objective is to minimize the total annualized production costs, maximize the percentage of renewable energy sources in the total power production and minimize the wind power curtailed. To solve this optimization problem, a derivative free multiobjective optimization method (Direct MultiSearch) is used. This methodology is applied to the integrated power and water supply system proposed for the island of S. Vicente, in Cape Verde. The results show that the penetration of renewable energy sources can reach 84% with a 27% decrease of power and water production costs and 67% decrease of CO 2 emissions, in relation to the values foreseen for 2020.

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Bi-objective optimization of a grid-connected decentralized energy system

et al. 2017

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Summary Motivated by the increasing transition from fossil fuel–based centralized systems to renewable energy–based decentralized systems, we consider a bi‐objective investment planning problem of a grid‐connected decentralized hybrid renewable energy system. In this system, solar and wind are the main electricity generation resources. A national grid is assumed to be a carbon‐intense alternative to the renewables and is used as a backup source to ensure reliability. We consider both total cost and carbon emissions caused by electricity purchased from the grid. We first discuss a novel simulation‐optimization algorithm and then adapt multi‐objective metaheuristic algorithms. We integrate a simulation module to these algorithms to handle the stochastic nature of this bi‐objective problem. We perform extensive comparative analysis for the solution approaches and report their performances in terms of solution time and quality based on well‐known measures from the literature.

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A dynamic MOPSO algorithm for multiobjective optimal design of hybrid renewable energy systems

Cited by 47 publications

References 27 publications

Optimal design of hybrid renewable energy systems in buildings with low to high renewable energy ratio

Optimal design of hybrid renewable energy systems in buildings with low to high renewable energy ratio

Optimization of a wind powered desalination and pumped hydro storage system

Bi-objective optimization of a grid-connected decentralized energy system

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